The present invention relates to a method for the recognition, in particular an accelerated recognition, of an object in an image, in particular in the course of video monitoring.
Object detectors are used in video analysis in order to afford the user additional functionality. This includes, for example, the recognition of suspicious objects or persons, numbers of persons or automatic monitoring of a specific area in the image.
The task of an object detector is to find objects in different sizes and positions in the image. For this purpose, it is routine to use classifiers which receive as input a vector comprising calculated features of an image segment. The output is a binary decision as to whether the considered image segment includes an object. In order to find objects at all positions in the image, the image segment is pushed over the image, and the object detector is applied at each site. In order, in addition, to find objects of different sizes, the object detector is applied to scaled versions of the image. The product of the number of scaling and the positions in the image yields the number of required classification runs, and influences the run time and the accuracy of the object detection.
A boosting classifier is one which is frequently used in image and video classification. As regards details in more depth, reference is made to relevant publications such as Y. Freund, R. Schapire. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, Vol. 55, 1997. So called weak classifiers are combined with one another in this case. A typical example of a weak classifier is a so called decision stump, for which the value of a single feature from the image segment is compared with a threshold value. If the value is above the threshold value, a positive output results, otherwise a negative one. The outputs of the weak classifiers are combined with one another in the case of the boosting classifier in order to give rise to a classification decision. However, this procedure requires very many classifications to be carried out, and this can be problematic for real time applications.
It is also possible to use a so called classification cascade such as is described in P. Viola, M. Jones. Rapid Object Detection using a Boosted Cascade of Simple Features. Proceedings Conference on Computer Vision and Pattern Recognition, Vol. 1, December 2001. Here, the image segment is classified with the aid of a sequence of boosting classifiers of growing complexity. The classification is terminated as soon as one of the classifiers outputs a negative decision. It is therefore usual for only a subset of the classifiers to be used, and this leads to savings in time. However, it is also necessary here to carry out very many classifications, and this can be problematic for real time applications.
There is, in addition, the possibility of restricting the search space in the image, for example by background recognition (for example, search for skin colorations in the color image of face recognition, see L. Torres, J. Y. Reutter, L. Lorente. The Importance of the Color Information in Face Recognition. International Conference on Image Processing, Kobe, Japan, 1999). However, such methods are detached from the actual method of object detection and produce additional run time.
It is therefore desirable to specify a possibility of accelerating object detection in an image, in particular regarding real time applications.